The field of the invention relates to systems and methods for ionizing radiation treatment. More particularly, the invention relates to systems and methods for intensity modulated proton therapy.
Lung cancer is the leading cancer killer worldwide, and about 30% to 40% of patients with locally advanced lung cancer require combined-modality treatment, including radiotherapy. However, with photon radiotherapy and concurrent chemotherapy, the 5-year survival is only about 15% for patients with stage III non-small cell lung cancer. Among those, about 50% experience local disease recurrence. Also, uncontrolled local disease may continue to seed to distant organs, resulting in distant metastasis and death. Hence, further improvements in survival of patients suffering from lung, and other types of cancer, require the safe delivery of higher radiation doses, which is not possible with photon radiotherapy.
Proton therapy approaches take advantage of the energy deposition profile of protons in tissue to provide highly conformal exposure of tumors while sparing organs-at-risk (“OAR”). Specifically, protons have a finite penetration depth in dependence of their energy, and the radiation dose delivered is maximum within a short distance of a proton's range, known as the Bragg peak. In particular, tissues located closer to the surface of a body receive less radiation dose compared to those at the Bragg peak. In addition, the dose profile drops dramatically beyond the Bragg peak, and hence tissues therein receive practically no radiation dose. As such, proton treatment systems make use of the dose deposition characteristics of protons in order to maximize tumor tissue coverage while minimizing dose to normal tissues as much as possible.
Unlike passive-scattering proton therapy (“PSPT”) techniques, which includes scattering materials placed along the path of protons in order to provide lateral field sizes and depth of coverage useful for clinical applications, intensity-modulated proton therapy (“IMPT”) uses magnetic steering of narrow proton beams, know as a beamlets, to deliver radiation dose to treatment target. Specifically, the energies and intensities of many individual beamlets, typically aimed at a target from multiple directions, are optimized using sophisticated computer algorithms that implement mathematically specified criteria, captured in what is known as an objective function. In the treatment of lung tumors, for example, this approach offers improved high-dose conformality when compared with PSPT, with better healthy tissue sparing in the mid- to low-dose range. Using such an objective function, an optimal compromise between adequate tumoricidal dose to a target and sparing of critical normal structures is achieved.
Several factors are known to change the dose received by target and normal structures as compared to the planned dose, thereby compromising the safety and efficacy of the treatment. For instance, inter-fractional variation in patient geometry, caused by tumor shrinkage, patient weight change, setup variation, proton range uncertainty, and so on, can cause significant differences in delivered dose distributions. Specifically, patient setup uncertainties can occur through the course of simulation and daily treatment. In addition, proton range uncertainty caused by tumor shrinkage, patient weight change, and computed tomography (“CT”) number and stopping power ratio uncertainties may significantly alter dose to target and surrounding healthy, or non-target tissues. Unlike uncertainties caused by respiratory motion, patient setup and range uncertainties include systematic uncertainties that can affect the delivery of an intended dose throughout the course of multi-fractionated treatment. Some methods adopted in current practice that aim to address significant inter-fractional anatomy changes include adaptive replanning approaches. However, the adaptive re-plan is not robust to other uncertainties and only based on the most recent weekly CT imaging, while the anatomy change in a subsequent treatment fraction is not considered.
Intra-fractional respiratory motion may also cause significant changes in patient geometry during treatment and, consequently, in dose distribution delivered. Some optimization methods have been proposed to handle respiratory motion assuming that anatomy moves in exactly the same way during treatment sessions as it does during the simulation session. However, motion can vary from day to day, and such assumptions can considerably degrade the resulting dose distribution administered to a patient. Specifically, to account for respiratory motion, an internal gross target volume (“GTV”) is conventionally formed to encompass the extent of GTV motion in all phases of a respiratory cycle using 4-dimensional (“4D”) CT images. The GTV is then expanded to form an internal target volume (“ITV”), which includes an additional margin (e.g., 8 mm for lung patients) to account for subclinical microscopic disease. In current practice using PSPT, the change in tissue density due to respiratory motion is mitigated by use of averaged 4D CT and integrated-GTV (over all respiratory phases) density override, which includes the assignment of the maximum CT HU number from individual phases. Although this approach has been shown effective in preserving target coverage under the influence of respiratory motion for PSPT, its validity in IMPT has not been proved. Also, other techniques, such as breath-hold, gating, and tumor tracking methods are also commonly used to mitigate the effects of more substantial motion.
Generally, for intensity-modulated radiotherapy (“IMRT”) approaches that provide treatment using photons, setup uncertainties are typically taken into account by adding isotropic margins to a clinical target volume (“CTV”), which reflects gross tumor and microscopic disease, to form a planning target volume (“PTV”). On the other hand, in PSPT, range and setup uncertainties are typically handled by adding lateral margins for setup uncertainties around the CTV and distal and proximal margins to account for range uncertainties. The PTV margin is chosen with the implicit assumption that the CTV will remain covered with the prescribed isodose surface with high probability (e.g., 95%) in the presence of the above described uncertainties. This is generally a good assumption for photons because the spatial distributions of photon dose are minimally perturbed by uncertainties. However, such approach of margin expansion may not be effective in IMPT due to the highly fluctuating nature of beamlet intensities and the resulting inhomogeneous dose distributions per beam in the target volume. Nevertheless, in the absence of suitable alternatives to assigning margins, the current practice in IMPT is to expand GTV to internal GTV and expand the CTV/ITV to form a PTV in a manner similar to the practice for photon therapy. As a result, the produced dose distributions are deficient in robustness and may cause IMPT dose distributions delivered to the patient to be significantly different from those prescribed, leading to unforeseen clinical consequences.
As many emerging proton treatment centers rely on pencil beam scanning technology, there is an increasing need for systems and methods for improved IMPT. Specifically, there is a need for systems and methods directed to quantifying a treatment plan's sensitivity to uncertainties, as well as mitigating the influence of such uncertainties, to deliver precise and predictable proton therapy with the highest clinical benefit.